905 research outputs found

    First-principles study of phonon linewidths in noble metals

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    Phonon lifetimes in Cu, Ag, and Au at low and high temperatures were calculated along high symmetry directions using density functional theory combined with second-order perturbation theory. Both harmonic and third-order anharmonic force constants were computed using a supercell small displacement method, and the two-phonon densities of states were calculated for all three-phonon processes consistent with the kinematics of energy and momentum conservation. A nonrigorous Grüneisen model with no q-dependence of the anharmonic coupling constants offers a simple separation of the potential and the kinematics, and proved semiquantitative for Cu, Ag, and Au. A rule is reported for finding the most anharmonic phonon mode in fcc metals

    Phonon anharmonicity of rutile TiO_2 studied by Raman spectrometry and molecular dynamics simulations

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    Raman spectra of rutile titanium dioxide (TiO_2) were measured at temperatures from 100 to 1150 K. Each Raman mode showed unique changes with temperature. Beyond the volume-dependent quasiharmonicity, the explicit anharmonicity was large. A new method was developed to fit the thermal broadenings and shifts of Raman peaks with a full calculation of the kinematics of three-phonon and four-phonon processes, allowing the cubic and quartic components of the anharmonicity to be identified for each Raman mode. A dominant role of phonon-phonon kinematics on phonon shifts and broadenings is reported. Force-field molecular dynamics calculations with the Fourier-transformed velocity autocorrelation method were also used to perform a quantitative study of anharmonic effects, successfully accounting for the anomalous phonon anharmonicity of the B_1_(g) mode

    Balanced material selection approach of 316 stainless steel for high pressure hydrogen systems

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    Anharmonicity-induced phonon broadening in aluminum at high temperatures

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    Thermal phonon broadening in aluminum was studied by theoretical and experimental methods. Using second-order perturbation theory, phonon linewidths from the third-order anharmonicity were calculated from first-principles density-functional theory (DFT) with the supercell finite-displacement method. The importance of all three-phonon processes were assessed and individual phonon broadenings are presented. The good agreement between calculations and prior measurements of phonon linewidths at 300 K and new measurements of the phonon density of states to 750 K indicates that the third-order phonon-phonon interactions calculated from DFT can account for the lifetime broadenings of phonons in aluminum to at least 80% of its melting temperature

    Compensation for Maritime Ecological Damages in China Judicial Practice

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    The Article discusses the judicial experience of compensation for maritime ecological damages in China. The discussion focusse on the verdict of “Tasman sea” oil spill case. Scope and methods of assessment of ecological damages are major part of the discussion. Because of the absence of legislation on compensation for maritime ecological damages, the verdict is a significant guide to similar case trial in the future

    Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learning

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    Auction-based Federated Learning (AFL) has attracted extensive research interest due to its ability to motivate data owners (DOs) to join FL through economic means. While many existing AFL methods focus on providing decision support to model users (MUs) and the AFL auctioneer, decision support for data owners remains open. To bridge this gap, we propose a first-of-its-kind agent-oriented joint Pricing, Acceptance and Sub-delegation decision support approach for data owners in AFL (PAS-AFL). By considering a DO's current reputation, pending FL tasks, willingness to train FL models, and its trust relationships with other DOs, it provides a systematic approach for a DO to make joint decisions on AFL bid acceptance, task sub-delegation and pricing based on Lyapunov optimization to maximize its utility. It is the first to enable each DO to take on multiple FL tasks simultaneously to earn higher income for DOs and enhance the throughput of FL tasks in the AFL ecosystem. Extensive experiments based on six benchmarking datasets demonstrate significant advantages of PAS-AFL compared to six alternative strategies, beating the best baseline by 28.77% and 2.64% on average in terms of utility and test accuracy of the resulting FL models, respectively

    STILN: A Novel Spatial-Temporal Information Learning Network for EEG-based Emotion Recognition

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    The spatial correlations and the temporal contexts are indispensable in Electroencephalogram (EEG)-based emotion recognition. However, the learning of complex spatial correlations among several channels is a challenging problem. Besides, the temporal contexts learning is beneficial to emphasize the critical EEG frames because the subjects only reach the prospective emotion during part of stimuli. Hence, we propose a novel Spatial-Temporal Information Learning Network (STILN) to extract the discriminative features by capturing the spatial correlations and temporal contexts. Specifically, the generated 2D power topographic maps capture the dependencies among electrodes, and they are fed to the CNN-based spatial feature extraction network. Furthermore, Convolutional Block Attention Module (CBAM) recalibrates the weights of power topographic maps to emphasize the crucial brain regions and frequency bands. Meanwhile, Batch Normalizations (BNs) and Instance Normalizations (INs) are appropriately combined to relieve the individual differences. In the temporal contexts learning, we adopt the Bidirectional Long Short-Term Memory Network (Bi-LSTM) network to capture the dependencies among the EEG frames. To validate the effectiveness of the proposed method, subject-independent experiments are conducted on the public DEAP dataset. The proposed method has achieved the outstanding performance, and the accuracies of arousal and valence classification have reached 0.6831 and 0.6752 respectively

    FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler

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    Federated learning (FL) enables collaborative machine learning across distributed data owners, but data heterogeneity poses a challenge for model calibration. While prior work focused on improving accuracy for non-iid data, calibration remains under-explored. This study reveals existing FL aggregation approaches lead to sub-optimal calibration, and theoretical analysis shows despite constraining variance in clients' label distributions, global calibration error is still asymptotically lower bounded. To address this, we propose a novel Federated Calibration (FedCal) approach, emphasizing both local and global calibration. It leverages client-specific scalers for local calibration to effectively correct output misalignment without sacrificing prediction accuracy. These scalers are then aggregated via weight averaging to generate a global scaler, minimizing the global calibration error. Extensive experiments demonstrate FedCal significantly outperforms the best-performing baseline, reducing global calibration error by 47.66% on average.Comment: This paper has been accepted by ICML'2
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